library_name: transformers
license: apache-2.0
base_model: google/byt5-small
language:
- sw
- zu
- lg
- ny
- sn
- kg
- ki
- kam
- suk
- mer
- ln
- nso
- xh
- nyf
- rn
- rw
tags:
- bantu
- morphology
- multilingual
- low-resource
- character-level
- byt5
- african-languages
- swahili
- zulu
- kikuyu
datasets:
- mutisya/bantu-words-26-03-v3.5
BantuMorph v7
BantuMorph is a character-level transformer for morphological analysis across 16 Bantu languages. Given a word in any of the supported languages, it can extract the lemma and morphological features, segment the word into morphemes, predict the noun class, or generate inflected forms from a lemma plus features.
The model is trained on 80,765 morphological paradigms across the 16 languages and operates over byte-level input, which lets it handle the rich agglutinative morphology of Bantu languages without word-piece tokenization artifacts.
Quick summary
| Property | Value |
|---|---|
| Architecture | ByT5-small (encoder-decoder, character-level) |
| Parameters | 300M |
| Languages | 16 Bantu languages |
| Tasks | 5 (extract, segment, lemmatize, nounclass, complete) |
| Base model | google/byt5-small |
| License | Apache-2.0 |
Languages
| Code | Language | Guthrie zone | Approx. speakers (M) |
|---|---|---|---|
| swh | Swahili | G42 | 200 |
| zul | Zulu | S42 | 12 |
| xho | Xhosa | S41 | 8 |
| sna | Shona | S10 | 9 |
| nso | N. Sotho | S32 | 4 |
| nya | Chichewa | N31 | 14 |
| kik | Kikuyu | E51 | 8 |
| kam | Kamba | E55 | 5 |
| mer | Kimeru | E54 | 4 |
| nyf | Giriama | E72b | 0.6 |
| kin | Kinyarwanda | J61 | 12 |
| run | Kirundi | JD62 | 9 |
| lug | Luganda | JE15 | 8 |
| kon | Kongo | H16 | 5 |
| lin | Lingala | C40 | 40 |
| suk | Kisukuma | F21 | 5 |
What the model does
BantuMorph supports five morphological tasks, each invoked through a task prefix on the input.
Task 1 — Extract (lemma + features)
Joint lemmatization and feature prediction.
Input: swh-extract: ninasoma
Output: soma V;PRS;1;SG
Task 2 — Segment
Morpheme boundary detection.
Input: swh-segment: ninasoma
Output: ni-na-soma
Task 3 — Lemmatize
Extract the citation form.
Input: swh-lemmatize: ninasoma
Output: soma
Task 4 — Noun class
Predict the Bantu noun class for a noun.
Input: swh-nounclass: mtoto
Output: BANTU1
Task 5 — Complete (inflection)
Generate an inflected form from a lemma and features.
Input: swh-complete: soma [V;PRS;1;SG]
Output: ninasoma
How to use
from transformers import T5ForConditionalGeneration, AutoTokenizer
model_id = "thiomi/bantumorph-v7"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = T5ForConditionalGeneration.from_pretrained(model_id)
def run(prompt: str) -> str:
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=128)
outputs = model.generate(**inputs, max_new_tokens=64)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Examples
print(run("swh-extract: ninasoma")) # 'soma V;PRS;1;SG'
print(run("swh-segment: ninasoma")) # 'ni-na-soma'
print(run("swh-lemmatize: ninasoma")) # 'soma'
print(run("swh-nounclass: mtoto")) # 'BANTU1'
print(run("swh-complete: soma [V;PRS;1;SG]")) # 'ninasoma'
The task prefix is the language ISO code followed by the task name, separated by a hyphen. The supported language codes are listed in the table above (e.g. swh-, kik-, zul-).
Evaluation
Evaluated on a held-out test set of 4,687 examples spanning all 16 languages and all 5 tasks (~290 examples per language on average, stratified by task).
Per-task accuracy
| Task | Accuracy |
|---|---|
| segment | 96.1% |
| nounclass | 87.8% |
| lemmatize | 82.3% |
| complete | 60.7% |
| extract | 42.9% |
| Overall | 67.1% |
Per-language accuracy (best to worst)
| Language | Accuracy |
|---|---|
| Shona | 94.4% |
| Chichewa | 89.6% |
| Luganda | 85.6% |
| Swahili | 83.2% |
| Kongo | 80.9% |
| (most other languages) | 60–80% |
| Northern Sotho | 44.7% |
For full per-task × per-language breakdown, see the BantuMorph paper.
Notes on the evaluation
- Accuracy is exact-match on the model output. For segmentation specifically, ~45% of "errors" on common training vocabulary are actually valid alternative segmentations rather than incorrect ones — see the BantuMorph paper for the over-segmentation analysis.
- Languages with smaller training corpora (Northern Sotho, Xhosa, Kirundi, Kinyarwanda) tend to underperform languages with larger corpora.
- The hardest task is
extractbecause of the large feature space; the easiest issegment.
Training data
BantuMorph v7 was trained on 80,765 morphological paradigms drawn from:
- UniMorph Bantu paradigm collections for the languages that have them
- LLM-generated paradigm extensions from related Bantu languages, validated by community linguists
- Cross-lingual transfer paradigms from high-resource Bantu languages (primarily Swahili, Zulu, and Luganda)
Data was split 85% train / 10% validation / 5% test, with care taken to ensure speaker-disjoint and lemma-disjoint splits where possible.
Limitations
- Not a substitute for native-speaker validation. The model is a useful starting point for morphological annotation, but generated outputs should be reviewed by speakers or linguists for any high-stakes use.
- Accuracy varies sharply by language. The 16 languages have very different amounts of training data; performance ranges from ~95% (Shona) to ~45% (Northern Sotho) overall.
- Out-of-distribution loanwords. The model can over-apply Bantu morphological templates to loanwords from English, Arabic, French, or Portuguese. Filtering loanwords is an open problem; see the related v3.5 dataset for one approach.
- No tone marking. The model treats text at the byte level and does not explicitly encode lexical tone. For tonal languages like Luganda, tonal distinctions are missing from both input and output.
- Limited orthographic coverage. Trained on standard Latin orthography for each language. Variant spellings (especially in less-standardized languages) may underperform.
- Single-word inputs. Each task expects a single word; running on multi-word phrases or full sentences will produce unreliable results.
Intended use
BantuMorph is intended for:
- Computational linguistics research on Bantu languages
- Prototyping morphological analyzers for under-resourced Bantu languages via cross-lingual transfer
- Educational tools that need morphological breakdown (lemmatization, segmentation, noun class)
- Pre-processing for downstream NLP pipelines (information retrieval, search, named entity recognition)
It is not intended for:
- Production speech-to-text or translation systems on its own
- Definitive linguistic analysis without human review
- Sociolinguistic or dialect-specific analysis
Related work
- Zero-shot morphological discovery — applies BantuMorph to Giriama with only 91 labeled paradigms. arxiv:2604.22723
- Neural recovery of historical lexical structure — uses BantuMorph embeddings to recover Proto-Bantu cognate structure. arxiv:2604.22730
Citation
If you use BantuMorph in your work, please cite:
@misc{mutisya2026bantumorph,
title = {BantuMorph: A Character-Level Transformer for Morphological Analysis Across 16 Bantu Languages},
author = {Hillary Mutisya and John Mugane},
year = {2026},
note = {Forthcoming on arXiv. Model available at \url{https://huggingface.co/thiomi/bantumorph-v7}}
}
Model card authors
Hillary Mutisya, John Mugane
Contact
For issues, questions, or collaboration, please open an issue on the model repository or contact the authors directly.